Identification of indoor and outdoor traffic usage of customers of a telecommunications network

ABSTRACT

Systems and methods to identify whether user traffic is generated indoors (e.g., from within a building) or outdoors for a variety of applications, including improving capacity planning, identifying new products offerings, troubleshooting/planning, competitive analysis, planning optimum locations of capacity planning solution deployment, traffic offload analysis, etc. are disclosed. The method receives and aggregates data from a variety of sources, including customer geolocation data, network data, street/building maps, indoor/outdoor classification of traffic, etc. to generate demand density maps that depict network traffic usage patterns at a building level. The method can then use the demand density maps to identify hotspots, evaluate in-building coverage, and select and rank optimum solutions and/or locations for capacity improvement solutions deployment. As a result, a telecommunications service provider is able to efficiently and economically identify targeted solutions and locations to expand capacity of cell sites and improve customer experiences.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application incorporates by reference U.S. application Ser. No.16/869,530, filed May 7, 2020, entitled MANAGEMENT OF TELECOMMUNICATIONSNETWORK CONGESTION ON ROADWAYS.

BACKGROUND

A telecommunications network is established via a complex arrangementand configuration of many cell sites that are deployed across ageographical area. For example, there can be different types of cellsites (e.g., macro cells, microcells, and so on) positioned in aspecific geographical location, such as a city, neighborhood, and soon). These cell sites strive to provide adequate, reliable coverage formobile devices (e.g., smart phones, tablets, and so on) via differentfrequency bands and radio networks such as a Global System for Mobile(GSM) mobile communications network, a code/time division multipleaccess (CDMA/TDMA) mobile communications network, a 3rd or 4thgeneration (3G/4G) mobile communications network (e.g., General PacketRadio Service (GPRS/EGPRS)), Enhanced Data rates for GSM Evolution(EDGE), Universal Mobile Telecommunications System (UMTS), or Long TermEvolution (LTE) network), 5G mobile communications network, IEEE 802.11(WiFi), or other communications networks. The devices can seek access tothe telecommunications network for various services provided by thenetwork, such as services that facilitate the transmission of data overthe network and/or provide content to the devices.

As device usage continues to rise at an impressive rate, there are toomany people using too many network (and/or data)-hungry applications inplaces where the wireless edge of the telecommunications network haslimited or no capacity. As a result, most telecommunications networkshave to contend with issues of network congestion. Network congestion isthe reduced quality of service that occurs when a network node carriesmore data than it can handle. Typical effects include queueing delay,packet loss or the blocking of new connections, overall resulting indegraded customer experience.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a suitable computing environmentwithin which to identify indoor/outdoor telecommunications networktraffic.

FIG. 2 is a block diagram illustrating the components of theindoor/outdoor traffic identification system.

FIG. 3 is a flow diagram illustrating a process of identifyingindoor/outdoor telecommunications network traffic.

FIGS. 4A-4F are example diagrams illustrating demand density mapsgenerated for identifying indoor/outdoor telecommunications networktraffic.

FIG. 5 shows a comparison between existing customer location versusspatially aggregating or joining customers within a building polygon.

FIG. 6 shows an example of a data table showing total data for abuilding after classification.

In the drawings, some components and/or operations can be separated intodifferent blocks or combined into a single block for discussion of someof the implementations of the present technology. Moreover, while thetechnology is amenable to various modifications and alternative forms,specific implementations have been shown by way of example in thedrawings and are described in detail below. The intention, however, isnot to limit the technology to the specific implementations described.On the contrary, the technology is intended to cover all modifications,equivalents, and alternatives falling within the scope of the technologyas defined by the appended claims.

DETAILED DESCRIPTION

An aim of a telecommunications service provider is to minimize customerexperience degradation. This is typically achieved by deployingcongestion management and/or network improvement solutions at one ormore cell sites. To combat network congestion, different capacityplanning solutions have been suggested to address and resolve thedegradation issues. Further, depending on whether a location is indoorsor outdoors, different network capacity solutions may be applicable toenhance network capacity. For example, for a building with high networkusage traffic, it can be beneficial to deploy microcells on specificbuilding floors to ease network traffic congestion in that building.But, it is currently difficult to identify whether network usage trafficis being generated indoors or outdoors, especially in high densityareas, such as downtowns. As a result, the process for identifying whichnetwork capacity solutions to deploy to alleviate network congestionand/or improve capacity is more of a trial and error process. And it canalso be difficult to predict traffic offload forecast. For example, itis difficult to determine/identify locations for hotspot solutions toalleviate network congestion. This results in inefficiencies as well aswasted costs as telecommunications service providers try (and fail)deploying sub-optimum network capacity solutions that are not tailoredto the indoor or outdoor location of network traffic usage.

To solve these and other problems, the inventors have developed systemsand methods to identify whether user traffic is generated indoors (e.g.,from within a building) or outdoors for a variety of applications,including improving capacity planning, identifying new productsofferings, troubleshooting/planning, competitive analysis, planningoptimum locations of capacity planning solution deployment, trafficoffload analysis, etc. The method receives and aggregates data from avariety of sources, including customer geolocation data, network data,street/building maps, indoor/outdoor classification of traffic, etc. togenerate demand density maps that depict network traffic usage patternsat a building level. The method can then use the demand density maps toidentify hotspots, evaluate in-building coverage, and select and rankoptimum solutions and/or locations for capacity improvement solutionsdeployment. As a result, a telecommunications service provider is ableto efficiently and economically identify targeted solutions andlocations to expand capacity of cell sites and improve customerexperiences. (While the term “customer” is used in the application, oneof skill in the art will understand that the concepts discussed hereinwill similarly apply to other users, who may or may not be customers ofa telecommunications service provider.)

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of implementations of the present technology. It will beapparent, however, to one skilled in the art that implementations of thepresent technology can be practiced without some of these specificdetails.

The phrases “in some implementations,” “according to someimplementations,” “in the implementations shown,” “in otherimplementations,” and the like generally mean the specific feature,structure, or characteristic following the phrase is included in atleast one implementation of the present technology and can be includedin more than one implementation. In addition, such phrases do notnecessarily refer to the same implementations or differentimplementations.

Suitable Computing Environments

FIG. 1 is a block diagram illustrating a suitable computing environmentwithin which to manage telecommunications network congestion onroadways.

One or more user devices 110, such as mobile devices or user equipment(UE) associated with users (such as mobile phones (e.g., smartphones),tablet computers, laptops, and so on), Internet of Things (IoT) devices,devices with sensors, and so on, receive and transmit data, streamcontent, and/or perform other communications or receive services over atelecommunications network 130, which is accessed by the user device 110over one or more cell sites 120, 125. For example, the mobile device 110can access a telecommunication network 130 via a cell site at ageographical location that includes the cell site, in order to transmitand receive data (e.g., stream or upload multimedia content) fromvarious entities, such as a content provider 140, cloud data repository145, and/or other user devices 155 on the network 130 and via the cellsite 120.

The cell sites can include macro cell sites 120, such as base stations,small cell sites 125, such as picocells, microcells, or femtocells,and/or other network access component or sites. The cell cites 120, 125can store data associated with their operations, including dataassociated with the number and types of connected users, data associatedwith the provision and/or utilization of a spectrum, radio band,frequency channel, and so on, provided by the cell sites 120, 125, andso on. The cell sites 120, 125 can monitor their use, such as theprovisioning or utilization of physical resource blocks (PRBs) providedby a cell site physical layer in LTE network; likewise the cell sitescan measure channel quality, such as via channel quality indicator (CQI)values, etc.

Other components provided by the telecommunications network 130 canmonitor and/or measure the operations and transmission characteristicsof the cell sites 120, 125 and other network access components. Forexample, the telecommunications network 130 can provide a networkmonitoring system, via a network resource controller (NRC) or networkperformance and monitoring controller, or other network controlcomponent, in order to measure and/or obtain the data associated withthe utilization of cell sites 120, 125 when data is transmitted within atelecommunications network.

In some implementations, the computing environment 100 includes aindoor/outdoor traffic identification system 150 configured to monitoraspects of the network 130 based on, for example, data received from thenetwork monitoring system. The indoor/outdoor traffic identificationsystem 150 can receive customer usage data, geospatial data, locationdata (e.g., latitude/longitude), map data (e.g., depictingstreets/buildings), building data (e.g., building type, wall thickness,signal attenuation, building configuration/layout, etc.), and networkdata to determine where and when congestion happens in a geographic area(e.g., in a city), whether the congestion is occurring indoors (e.g.,inside buildings), and/or during which time(s) of day.

FIG. 1 and the discussion herein provide a brief, general description ofa suitable computing environment 100 in which the indoor/outdoor trafficidentification system 150 can be supported and implemented. Although notrequired, aspects of the indoor/outdoor traffic identification system150 are described in the general context of computer-executableinstructions, such as routines executed by a computer, e.g., mobiledevice, a server computer, or personal computer. The system can bepracticed with other communications, data processing, or computer systemconfigurations, including: Internet appliances, hand-held devices(including tablet computers and/or personal digital assistants (PDAs)),Internet of Things (IoT) devices, all manner of cellular or mobilephones, multi-processor systems, microprocessor-based or programmableconsumer electronics, set-top boxes, network PCs, mini-computers,mainframe computers, and the like. Indeed, the terms “computer,” “host,”and “host computer,” and “mobile device” and “handset” are generallyused interchangeably herein, and refer to any of the above devices andsystems, as well as any data processor.

Aspects of the system can be embodied in a special purpose computingdevice or data processor that is specifically programmed, configured, orconstructed to perform one or more of the computer-executableinstructions explained in detail herein. Aspects of the system can alsobe practiced in distributed computing environments where tasks ormodules are performed by remote processing devices, which are linkedthrough a communications network, such as a Local Area Network (LAN),Wide Area Network (WAN), or the Internet. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

Aspects of the system can be stored or distributed on computer-readablemedia (e.g., physical and/or tangible non-transitory computer-readablestorage media), including magnetically or optically readable computerdiscs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductorchips), nanotechnology memory, or other data storage media. Indeed,computer implemented instructions, data structures, screen displays, andother data under aspects of the system can be distributed over theInternet or over other networks (including wireless networks), on apropagated signal on a propagation medium (e.g., an electromagneticwave(s), a sound wave, etc.) over a period of time, or they can beprovided on any analog or digital network (packet switched, circuitswitched, or other scheme). Portions of the system reside on a servercomputer, while corresponding portions reside on a client computer suchas a mobile or portable device, and thus, while certain hardwareplatforms are described herein, aspects of the system are equallyapplicable to nodes on a network. In alternative implementations, themobile device or portable device can represent the server portion, whilethe server can represent the client portion.

In some implementations, the user device 110 and/or the cell sites 120,125 can include network communication components that enable the devicesto communicate with remote servers or other portable electronic devicesby transmitting and receiving wireless signals using a licensed,semi-licensed, or unlicensed spectrum over communications network, suchas network 130. In some cases, the communication network 130 can becomprised of multiple networks, even multiple heterogeneous networks,such as one or more border networks, voice networks, broadband networks,service provider networks, Internet Service Provider (ISP) networks,and/or Public Switched Telephone Networks (PSTNs), interconnected viagateways operable to facilitate communications between and among thevarious networks. The telecommunications network 130 can also includethird-party communications networks such as a Global System for Mobile(GSM) mobile communications network, a code/time division multipleaccess (CDMA/TDMA) mobile communications network, a 3rd or 4thgeneration (3G/4G) mobile communications network (e.g., General PacketRadio Service (GPRS/EGPRS)), Enhanced Data rates for GSM Evolution(EDGE), Universal Mobile Telecommunications System (UMTS), or Long TermEvolution (LTE) network), 5G mobile communications network, IEEE 802.11(WiFi), or other communications networks. Thus, the user device isconfigured to operate and switch among multiple frequency bands forreceiving and/or transmitting data.

Further details regarding the operation and implementation of theindoor/outdoor traffic identification system 150 will now be described.

Examples of Identifying Indoor/Outdoor Network Traffic

FIG. 2 is a block diagram illustrating the components of theindoor/outdoor traffic identification system 150. The indoor/outdoortraffic identification system 150 can include functional modules thatare implemented with a combination of software (e.g., executableinstructions, or computer code) and hardware (e.g., at least a memoryand processor). Accordingly, as used herein, in some examples a moduleis a processor-implemented module or set of code, and represents acomputing device having a processor that is at least temporarilyconfigured and/or programmed by executable instructions stored in memoryto perform one or more of the specific functions described herein. Forexample, the indoor/outdoor traffic identification system 150 caninclude a location data module 210, a customer data module 220, anetwork data module 230, a location-based congestion identificationmodule 240, and a customer experience improvement module 250, each ofwhich is discussed separately below.

Location Data Module

The location data module 210 is configured and/or programmed to receivelocation related data (e.g., geospatial data). For example, the locationdata module 210 collects/receives/accesses one or more of the followinglocation related records, such as map data (e.g., depictingstreets/buildings), building map, building location (e.g.,latitude/longitude), building shape, building span (e.g., size ofbuilding—length, width, height), building type, wall thickness, signalattenuation, building configuration/layout, building floors,indoor/outdoor classification of traffic, signal attenuation, customer'sproximate location in building (e.g., floor number, proximity towindows, etc.), and so on. One or more location data records can beassociated with a timestamp to indicate that the data values correspondto the particular date/time.

The location records can be received from multiple sources and can beused to determine or classify whether a particular location (e.g., usinglatitude and longitude) is indoors or outdoors. For example, thelocation data module 210 can receive location records and/or locationclassification information (e.g., whether a location isindoors/outdoors) from one or more of the following sources: customerselection, APIs, applications on customer devices, sensor data fromcustomer devices (IoT, home broadband, femtocells), third partyapplications (e.g., OpenStreetMap®, Google Maps®, etc.), and so on. Insome implementations, when a customer uses a telecommunications networkprovider service (e.g., makes a call, opens a browser/application,etc.), the customer can select whether he/she is indoors/outdoors. Thecustomer selection data can be collected for a period of time and thenbe used to train machine learning models (e.g., decision trees, neuralnetworks, etc.) to predict whether customer traffic is generatedindoors/outdoors. Additionally or alternatively, one or more sensors ona customer's devices (IoT/broadband/Femto/Micro/PicoCells) can collectnetwork data, which can then be used in conjunction with the customer'slocation data (e.g., latitude/longitude) to determine/classify whetherthe customer is indoors/outdoors when a network data record wasgenerated. For example, data from temperature and/or humidity sensors(IoT) (which measure ambient temperature and/or humidity) from acustomer's device can be used to determine that the customer isindoors/inside a building (e.g., when the measured temperature is lowerthan the expected outdoors temperature, etc.) As most of theIoT/broadband modem/Femto/Micro/PicoCells) installed inside andmeasurement from them help to train the model for the whole traffic.

In some implementations, a third party application (e.g.,OpenStreetMap®) in conjunction with a customer's location (e.g.,latitude/longitude) can be used to perform a special join (e.g.,geospatial matches) to determine whether the customer's location isindoors/outdoors. As another example, a third-party API (e.g., Google®API) can be used to determine whether a customer's location isindoors/outdoors. Additionally, or alternatively, the distance between acustomer's device and one or more cell sites can also be used todetermine the location (indoors/outdoors) of the customer.

The location data module 210 can reconcile the location dataclassification (i.e., whether a location is indoors/outdoors), which isreceived from the various sources discussed above, to determine anaggregate location data classification for the location. For example,location data classification from two or more sources can be given anequal vote to determine whether location is indoors/outdoors. When amajority of the solutions determine that the location is indoors, thenthe aggregate location classification is determined to be indoors.Alternatively or additionally, the sources can be assigned weights(e.g., based on one or more factors, such as their accuracy,reliability, freshness, etc.), which are then used to compute anweighted aggregate location classification for a location.

FIG. 5 shows on the left that locations of customers previously wasidentified simply based on location data of individual mobile devices,without any joining our aggregation. However, as shown on the right,employing spatial joining within a building, traffic clustered within abuilding can be identified. As shown and explained herein, the presentsystem creates demand density maps at a building level to identifyhotspots. In-building coverage allows the system to determine an optimalindoor coverage solution to improve customer experience. As shown in thediagram to the right, the black building has greater than 10% of theindoor traffic for the location displayed on the map, and thusidentifies the best candidate for a carrier, building or user to employa solution to increase indoor capacity.

Customer Data Module

The customer data module 220 is configured and/or programmed to receivea customer's data when accessing services/utilities associated with atelecommunications network. For example, the customer data module 220collects/receives/accesses one or more of the following customer datarecords associated with a customer relating to the following types ofinformation (which can be stored in the Indoor/outdoor location database255): location specific records (LSR), call data records (CDRs), timingadvance values, RF signal data (e.g., e.g., Reference Signal ReceivedPower (RSRP) data, Reference Signal Received Quality (RSRQ) data),speed, experience throughputs, reported coverage, distance between thecustomer and at least one telecommunications network site, strength ofsignal, quantity of data used, type of device of the customer,applications data (e.g., application type, name, owner, manager, datasent/received/used/saved, bandwidth used, APIs accessed, etc.), sourceof customer records (for example, telecommunications service provider,third-party, application owner, etc.). Examples of other types of datacollected by the customer data module include, but are not limited to,data collected from third party applications (e.g., includingcrowdsourced data) that can help to determine customer experience withlocation. For example, the customer data module can collect informationof a user's location using his/her social media posts (e.g., tweets,check-ins, posts, etc.). As another example, the customer data modulecollects application level data (e.g., collected using applicationsrelated to Internet of Things (IoT) devices, sensors, billing meters,traffic lights, etc.) to identify the user location and applicationsused to enhance the location algorithm. The customer data recordsassociated with the customer can comprise information about anassociated customer location and an associated timestamp. For example, acall data record for a customer can identify a customer location and atimestamp when the call was initiated. The customer data module cancollect customer records that span a particular period of time dependingon, for example, density of customer records, customer activity, typesof customer records (for example, text, voice, video, app-usage,emergency services, etc.), services/products to be offered to thecustomer, types of customer experience enhancement solutions/actions tobe implemented, source of customer records, and so on. In someimplementations, the location and timestamp information can bedetermined using data gathered/generated by applications on a customer'smobile device (e.g., Spotify®, Pandora®, Facebook®, Twitter®, emailapplications, and so on). Other sources of information includecall/charge detail record (CDR), LSR, Social Media APIs, IoT devices aresources of customer data information.

Network Data Module

The network data module 230 is configured and/or programmed to receivenetwork data, such as timing advance values, site coverage/RF signaldata (e.g., Reference Signal Received Power (RSRP) data, ReferenceSignal Received Quality (RSRQ) data), channel quality indicator (CQI)values, capacity on site (configured bandwidth, used bandwidth, etc.),number of users, and so on. Other types/attributes of customerinformation that could be collected and/or would be useful can be fromnetwork data indicating how much data was transferred with low MCS(Modulation Coding and Scheduling). If most data transfer is through thelower MCS, this can mean that a customer is indoor or on the cell edgewhich also help to model the indoor and outdoor traffic.

Location-Based Congestion Identification Module

The location-based congestion identification module 240 is configuredand/or programmed to determine, based on customer data, network data,and/or location data, indoor network traffic congestion (e.g., withinbuildings in a city area). Determining indoor network traffic congestioncan aid with network troubleshooting/planning, performing competitiveanalysis, plan optimum location for deploying one or more customerexperience enhancement actions, perform traffic offload analysis,network demand (traffic and customer located in each building), and soon (discussed in more detail below).

Various metrics and mechanisms can be used to identify congestion at alocation. For example, U.S. Pat. No. 10,524,158, the contents of whichare incorporated herein in their entirety, describe systems and methodsfor identifying congestion at a cell site. For example, thelocation-based congestion identification module 240 computes aggregatecongestion values that for the following metrics: traffic, signalstrength (e.g., RF signal-RSRP), download speed, PRB (Physical resourceblock), total number of users in that location, and so on. Afterdetermining congestion at various locations using the customer dataand/or network data, the location-based congestion identification module240 can perform a special join of the congestion determination data withthe location data to determine whether the congestion is indoors oroutdoors.

FIG. 6 shows an example use case that shows a snapshot of the customerdata and location data, and how it is joined to produce indoors/outdoorscongestion information. As shown, certain buildings are listed andidentified by a cell name (CELL_NAME), and have traffic data associatedtherewith, such as megabytes of downlink and uplink in-building traffic(DL_INBUILDING_MB, UL_INBUILDING_MB), downlink and uplink traffic bycell sector (DL_SECTOR_MB, UL_SECTOR_MB), average reference signalsreceived power (AVG_RSRP), sub count and market name. In this manner,the location-based congestion identification module 240 can identifycongested buildings in a city. For example, when one or more aggregatecongestion values for a building are outside of threshold bound ranges,then the building is classified as congested.

FIG. 4A illustrates an example of geospatially combining customer and/ornetwork data 402 and location data 404 to generate demand density maps408 a and 408 b depicting congestion metric values at a building level.Demand density map 408 a depicts various buildings in a geographic area,each of which is visually marked to indicate the aggregate amount ofin-building traffic in the building. Similarly, demand density map 408 bdepicts various buildings in a geographic area, each of which isvisually marked to indicate the aggregate amount of in-building RFsignal data (RSRP) in the building.

The location-based congestion identification module 240 collectscustomer data and/or network data for multiple customers over a periodof time (e.g., a day, week, month, quarter, yearly, etc.). In someimplementations, the location-based congestion identification module 240performs outlier analysis on the customer data, location data, and/ornetwork data before using that information to determine indoor networktraffic congestion. In some implementations, the location-basedcongestion identification module 240 can determine indoor networktraffic congestion information for multiple buildings (e.g., allbuildings in a downtown area of a city), which can then be used toforecast future congestion in those buildings, similar buildings, and soon. For example, the information generated by location-based congestionidentification module 240 can be combined with data from other sources(such as network data, user application (e.g., Pandora®, Spotify®,etc.), speed testing applications (e.g., Ookla®), etc. to understand,learn, and forecast customer experience at a location. One or more ofthe following data points can be used to forecast customer experience:customer handset type, throughput, RSRP, hour of the day, location(latitude/longitude), distance from sites, capacity on site, CQI,traffic per site, Physical Resource Block (PRB), bandwidth, and so on.The location-based congestion identification module 240 can use a model(e.g., decision trees) trained on these data point values to determineindoors/outdoors location and/or congestion for new customer data.

Customer Experience Improvement Module

The customer experience improvement module 250 is configured and/orprogrammed to identify at least one customer experience enhancementaction. The customer experience enhancement actions are intended toenhance overall customer experience based upon the determined indoornetwork traffic congestion. After identifying congested buildings (e.g.,buildings having an aggregate congestion metric value that is greaterthan a threshold amount), the customer experience improvement module 250identifies the site(s) supporting the congested buildings as potentialsites where customer experience enhancement actions can be deployed toreduce congestion and improve customer experience. By identifyingwhether congestion is occurring indoors versus outdoors, the customerexperience improvement module 250 is able to identify and selectcustomer experience enhancement actions that are more targeted tosolving the congestion problems, thus optimizing solution deploymenttime, cost, and/or effort. For example, instead of identifying asector-add customer experience enhancement action for a congested site(which is both costly and takes more time to deploy), the customerexperience improvement module 250 offers a small cell customerexperience enhancement action for the congested site (which is cheaperand faster to deploy than the sector-add solution) because most of thecongestion is occurring indoors. Other examples of how the presentsystem can identify targeted congestion management solutions includeindoor congestion management, such as for DAS (Distributed AntennaSystems), Small Cells (1 km range), Femto Cells (400 m range),Micro/Pico Cells (100 m).

In some implementations, the customer experience enhancement actions areidentified based on a level of service subscribed to by the customer(e.g., basic service, premium service, etc.), customer's paymenthistory, customer location, time of day, promotions, coverage servicelevel, and so on. Examples of customer experience enhancement actioninclude, but are not limited to: adding spectrum to sites, removingspectrum from sites, adding cell site proximate to the sites, removingcell site(s), displacing cell site(s), adding or enhancing at least onetechnology capability for a site, implementing a cell split, deploying asmall cell, adding/removing a sector, enhancing sector capacity,adding/removing a cell on wheels, adding/removing a tower,adding/removing hot spots, modifying capacity at the identified at leastone site, and so on. Additionally or alternatively, the customerexperience enhancement action comprises providing one or more of thefollowing services to the customer (free or at reduced rates for aperiod of time): gaming, home security, music, videos, advertising,offers, rebates, location intelligence, upsales, partnerships with othercompanies, special content. For example, based on a customer's location(indoor/outdoor), the customer experience improvement module identifiesoffers for services such as, 4K video streaming services, gamingservices, and so on.

The customer experience improvement module 250 can further present ademand density map to customers that depicts various aggregatecongestion metric values for buildings. For example, as illustrated inFIG. 4B, a demand density map 408 c can be displayed. The demand densitymap 408 c depicts percentage of indoor traffic for buildings in a cityportion. The buildings can be visually graded (and/or color coded) todepict the amount of traffic in each building. A user can select abuilding (e.g., by hovering over it, clicking it, and so on) to viewdetails 410, such as the amount of traffic, signal strength, whether thebuilding is a candidate for deploying customer experience enhancementactions, which customer experience enhancement actions to deploy, and soon). Similarly, FIG. 4C illustrates a density map 408 d that depictsin-building traffic for various areas in a city (e.g., New York City). Auser can select a city portion (e.g., by zooming-in) to view additionaldetails for the selected portion. For example, as illustrated in FIG.4D, a user can view, for the Financial District in New York City,additional details, such as congested sector and building-level details412, the top 10 buildings contributing to sector congestion 414, and thedemand density map 408 e.

In some implementations, the customer experience improvement module 250can identify, select, and/or rank customer experience enhancementactions using values for multiple aggregate congestion metrics (e.g.,both traffic and signal strength). For example, as illustrated in FIG.4E, the customer experience improvement module 250 displays two demanddensity maps 408 f and 408 g for the same set of buildings 416, each ofwhich display congestion metric values for traffic and signal strength,respectively. The customer experience improvement module 250 can furtherdetermine, based on this congestion metrics information, that building420 has both high traffic and low signal strength. The customerexperience improvement module 250 can then identify building 420 as apotential candidate where customer experience enhancement actions shouldbe deployed. The customer experience improvement module 250 can furtherselect and/or rank the available customer experience enhancement actionsto be deployed at building 420 based on factors, such as buildinglocation, building type, wall thickness, signal attenuation, buildingconfiguration/layout, other nearby deployed solutions, and so on.

Additionally or alternatively, the customer experience improvementmodule 250 can identify indoors congestion in buildings across a widegeographic area (e.g., a state, country, etc.). For example, asillustrated in FIG. 4F, the customer experience improvement module 250can identify congestion at indoor venues across America and display amap 450 identifying the top n congested venues across the country.

Other use cases or applications include improving inbuilding coveragewhich thereby improves customer experience at indoor venues such asstadium, airports, shopping malls, museums, university/colleges,libraries, city halls, train stations and other public hotspots.

The customer experience improvement module 250 can select one or morecustomer experience enhancement actions and rank them according to oneor more of the following factors: customer preferences, cost ofimplementation of action, timeline of implementation of action, customerlocation, discount offered, cost to deploy, time to deploy, and so on.In some implementations, the customer experience improvement module 250transmits a list of selected customer experience enhancement actions tothe telecommunications service provider so that one or more of theselected actions can be implemented to enhance the overall customerexperience.

Flow Diagram

FIG. 3 is a flow diagram illustrating a process of identifyingindoor/outdoor telecommunications network traffic to enhance acustomer's experience with a telecommunications service provider.Process 300 begins at block 305 where it receives a set of customer datarecords (as discussed above in reference to the customer data module220) and/or a set of network data records (as discussed above inreference to the network data module 230). At block 310, process 300further receives/accesses/collects a set of location related datarecords (as discussed above in reference to the location data module210). Process 300 then proceeds to block 315 where it classifies one ormore of the customer and/or network data records as indoors or outdoorstraffic measurements (as discussed above in reference to the locationdata module and the location-based congestion identification module). Atblock 320, process 300 generates measurements for one or more congestionmetrics (e.g., at a building level), such as those discussed above inreference to the location-based congestion identification module. Usingthe generated congestion metric values, at block 325, process 300generates one or more demand density maps at a building level forbuildings in a geographic portion. After identifying indoors networkcongestion (e.g., building-level congestion), at block 330, process 300can identify, select, and/or rank customer experience enhancementactions to be implemented at one or more congested buildings, sitesassociated with the congested buildings, in the vicinity of thecongested buildings, and so on.

Conclusion

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof, means any connection or coupling,either direct or indirect, between two or more elements; the coupling ofconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, shall referto this application as a whole and not to any particular portions ofthis application. Where the context permits, words in the above DetailedDescription using the singular or plural number can also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above detailed description of implementations of the system is notintended to be exhaustive or to limit the system to the precise formdisclosed above. While specific implementations of, and examples for,the system are described above for illustrative purposes, variousequivalent modifications are possible within the scope of the system, asthose skilled in the relevant art will recognize. For example, somenetwork elements are described herein as performing certain functions.Those functions could be performed by other elements in the same ordiffering networks, which could reduce the number of network elements.Alternatively, or additionally, network elements performing thosefunctions could be replaced by two or more elements to perform portionsof those functions. In addition, while processes, message/data flows, orblocks are presented in a given order, alternative implementations canperform routines having blocks, or employ systems having blocks, in adifferent order, and some processes or blocks can be deleted, moved,added, subdivided, combined, and/or modified to provide alternative orsubcombinations. Each of these processes, message/data flows, or blockscan be implemented in a variety of different ways. Also, while processesor blocks are at times shown as being performed in series, theseprocesses or blocks can instead be performed in parallel, or can beperformed at different times. Further, any specific numbers noted hereinare only examples: alternative implementations can employ differingvalues or ranges.

The teachings of the methods and system provided herein can be appliedto other systems, not necessarily the system described above. Theelements, blocks and acts of the various implementations described abovecan be combined to provide further implementations.

Any patents and applications and other references noted above, includingany that can be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the technology can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of thetechnology.

These and other changes can be made to the invention in light of theabove Detailed Description. While the above description describescertain implementations of the technology, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system can varyconsiderably in its implementation details, while still beingencompassed by the technology disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the technology should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the technology with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific implementationsdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed implementations, butalso all equivalent ways of practicing or implementing the inventionunder the claims.

While certain aspects of the technology are presented below in certainclaim forms, the inventors contemplate the various aspects of thetechnology in any number of claim forms. For example, while only oneaspect of the invention is recited as implemented in a computer-readablemedium, other aspects can likewise be implemented in a computer-readablemedium. Accordingly, the inventors reserve the right to add additionalclaims after filing the application to pursue such additional claimforms for other aspects of the technology.

The invention claimed is:
 1. A computer-implemented method fordetermining indoor traffic usage for users of a wirelesstelecommunications service network, the method comprising: receivingcustomer traffic data records comprising information about userlocations and at least one of: wireless traffic data at the userlocations, upload speed data at the user locations, download speed dataat the user locations, wireless data throughput at the user locations,or received signal power at the user locations; accessing geospatialinformation corresponding to a geographic area; for at least a subset ofthe received customer traffic data records, classifying each record asan indoor traffic record or an outdoor traffic record using the receivedcustomer traffic data records and the accessed geospatial information;generating at least one demand density map for a set of buildings in thegeographic area, wherein the at least one demand density map depictsnetwork traffic usage patterns for each building in the set ofbuildings; evaluating telecommunications network coverage for eachbuilding in the set of buildings; and based on the evaluation,identifying at least one customer experience enhancement action capableof being deployed at a building in the set of buildings to improvecustomer usage of services provided via the wireless telecommunicationsservice network.
 2. The computer-implemented method of claim 1, whereinthe at least one customer experience enhancement action is furtheridentified to meet a particular coverage service level.
 3. Thecomputer-implemented method of claim 1, wherein generating the at leastone demand density map further accounts for at least one of thefollowing parameters: type of building, thickness of walls of building,number of floors of building, building dimensions, signal attenuation,proximate user location in building, or any combination thereof.
 4. Thecomputer-implemented method of claim 1, wherein the at least onecustomer experience enhancement action comprises: adding spectrum to atelecommunication site, removing spectrum from a telecommunication site,adding cell site proximate to a telecommunication site, removing cellsite proximate to a telecommunication site, displacing cell siteproximate to a telecommunication site, adding or enhancing at least onetechnology capability for a telecommunication site, cell split, smallcell deployment, sector addition, sector removal, sector capacityenhancement, cell on wheels addition, cell on wheel removal, toweraddition, tower removal, hot spots addition, hot spots removal, capacitymodification at a telecommunication site, or any combination thereof. 5.The computer-implemented method of claim 1, wherein the set of customertraffic data records comprises data generated by one or moreapplications executing on a mobile device of a customer, and wherein theone or more applications include a media streaming application, a socialmedia application, or an email or SMS/MMS application.
 6. Thecomputer-implemented method of claim 1, wherein the set of customertraffic data records comprises: location specific records (LSR), calldata records (CDRs), timing advance values, RF signals, distance betweenthe customer and at least one telecommunications network site, strengthof signal received by at least one device of the customer, quantity ofdata used by the at least one device of the customer, type of the atleast one device of the customer, or any combination thereof.
 7. Anapparatus for use with a wireless telecommunications service network,the apparatus comprising: at least one data processor; at least onecommunications module coupled to the at least one processor and to thewireless telecommunications service network; and at least one memory,communicatively coupled to the at least one processor, and storinginstructions to be executed by the at least one processor, theinstructions comprising: receiving customer traffic data recordscomprising information about user locations and at least one of: trafficdata, upload data, download data, throughput, or signal power; accessinggeospatial information corresponding to a geographic area; for at leasta subset of the received customer traffic data records, classifying eachrecord as an indoor traffic record or an outdoor traffic record usingthe received customer traffic data records and the accessed geospatialinformation; and generating at least one demand density map for a set ofbuildings in the geographic area, wherein the at least one demanddensity map depicts network traffic usage patterns for each building inthe set of buildings.
 8. The apparatus of claim 7, wherein theinstructions further comprise: evaluating telecommunications networkcoverage for each building in the set of buildings; and based on theevaluation, identifying at least one customer experience enhancementaction capable of being deployed at a building in the set of buildings.9. The apparatus method of claim 8, wherein the at least one customerexperience enhancement action is further identified to meet a particularcoverage service level.
 10. The apparatus of claim 7, wherein generatingthe at least one demand density map further accounts for at least one ofthe following parameters: type of building, thickness of walls ofbuilding, number of floors of building, building dimensions, signalattenuation, proximate user location in building, or any combinationthereof.
 11. The apparatus of claim 7, wherein the at least one customerexperience enhancement action comprises: adding spectrum to atelecommunication site, removing spectrum from a telecommunication site,adding cell site proximate to a telecommunication site, removing cellsite proximate to a telecommunication site, displacing cell siteproximate to a telecommunication site, adding or enhancing at least onetechnology capability for a telecommunication site, cell split, smallcell deployment, sector addition, sector removal, sector capacityenhancement, cell on wheels addition, cell on wheel removal, toweraddition, tower removal, hot spots addition, hot spots removal, capacitymodification at a telecommunication site, or any combination thereof.12. The apparatus of claim 7, wherein the set of customer traffic datarecords comprises data generated by one or more applications executingon a mobile device of a customer, and wherein the one or moreapplications include a media streaming application, a social mediaapplication, or an email or SMS/MMS application.
 13. The apparatus ofclaim 7, wherein the set of customer traffic data records comprises:location specific records (LSR), call data records (CDRs), timingadvance values, RF signals, distance between the customer and at leastone telecommunications network site, strength of signal received by atleast one device of the customer, quantity of data used by the at leastone device of the customer, type of the at least one device of thecustomer, or any combination thereof.
 14. At least one computer-readablemedium, carrying instructions, that when executed by at least one dataprocessing platform, determine indoor traffic usage for users of awireless telecommunications service network, the instructionscomprising: receiving customer traffic data records comprisinginformation about user locations and at least one of: wireless trafficdata at the user locations, upload speed data at the user locations,download speed data at the user locations, wireless data throughput atthe user locations, or received signal power at the user locations;accessing geospatial information corresponding to a geographic area; forat least a subset of the received customer traffic data records,classifying each record as an indoor traffic record or an outdoortraffic record using the received customer traffic data records and theaccessed geospatial information; and generating at least one demanddensity map for a set of buildings in the geographic area, wherein theat least one demand density map depicts network traffic usage patternsfor each building in the set of buildings; evaluating telecommunicationsnetwork coverage for each building in the set of buildings; and based onthe evaluation, identifying at least one customer experience enhancementaction capable of being deployed at a building in the set of buildingsto improve customer usage of services provided via the wirelesstelecommunications service network.
 15. The computer-readable medium ofclaim 14, wherein the at least one customer experience enhancementaction is further identified to meet a particular coverage servicelevel.
 16. The computer-readable medium of claim 14, wherein generatingthe at least one demand density map further accounts for at least one ofthe following parameters: type of building, thickness of walls ofbuilding, number of floors of building, building dimensions, signalattenuation, proximate user location in building, or any combinationthereof.
 17. The computer-readable medium of claim 14, wherein the atleast one customer experience enhancement action comprises: addingspectrum to a telecommunication site, removing spectrum from atelecommunication site, adding cell site proximate to atelecommunication site, removing cell site proximate to atelecommunication site, displacing cell site proximate to atelecommunication site, adding or enhancing at least one technologycapability for a telecommunication site, cell split, small celldeployment, sector addition, sector removal, sector capacityenhancement, cell on wheels addition, cell on wheel removal, toweraddition, tower removal, hot spots addition, hot spots removal, capacitymodification at a telecommunication site, or any combination thereof.18. The computer-readable medium of claim 14, wherein the set ofcustomer traffic data records comprises data generated by one or moreapplications executing on a mobile device of a customer, and wherein theone or more applications include a media streaming application, a socialmedia application, or an email or SMS/MMS application.
 19. Thecomputer-readable medium of claim 14, wherein the set of customertraffic data records comprises: location specific records (LSR), calldata records (CDRs), timing advance values, RF signals, distance betweenthe customer and at least one telecommunications network site, strengthof signal received by at least one device of the customer, quantity ofdata used by the at least one device of the customer, type of the atleast one device of the customer, or any combination thereof.